Pedestrian detection in images with complex backgrounds is valuable in theory and applications.To deal with the variability of illumination and pedestrian’s poses

the local feature based on vision attention (LFVA)

derived from a visual saliency mechanism

is proposed in this paper.LFVA is illumination and rotation invariant

and can also be used for multi-scale analysis.A pedestrian model based on feature blocks is proposed

in which the pedestrians are represented by a set of feature blocks.Every feature block is represented by the position and histogram of the LFVA.The pedestrian model is obtained by clustering.The AdBoost detection classifier is trained using the maximal responses of the feature blocks and is improved using hard negative samples and trusted samples.The local maximal of the response of the detection classifier in image and scale space is located as the position of the pedestrian by sliding window search.Compared with the existed methods

the proposed method is less sensitive to vertical edges and can deal with occlusion and pose variation to some extent.